Local analysis server, central analysis server, and data analysis method

Abstract

A local analysis server includes: a communicator for communicating with a plurality of devices and a central analysis server; and a controller for transmitting data collected from the plurality of devices to the central analysis server, receiving an analysis model including cluster information on a plurality of clusters generated by performing a clustering analysis on the collected data from the central analysis server, reconstructing the plurality of clusters based on the analysis model, and identifying a cluster corresponding to the received data from among the reconstructed clusters through a clustering analysis on the data received from the plurality of devices.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application claims priority to and the benefit of Korean Patent Application No. 10-2016-0148306 filed in the Korean Intellectual Property Office on Nov. 8, 2016, the entire contents of which are incorporated herein by reference.BACKGROUND(a) Field[0002]An exemplary embodiment of the present invention relates to a local analysis server, a central analysis server, and a data analysis method. More particularly, the exemplary embodiment of the present invention relates to a local analysis server, a central analysis server, and a data analysis method for classifying and analyzing data.(b) Description of the Related Art[0003]Sensor data are periodically collected and stored in an Internet of Things (IoT) environment. There may be many data that are beyond prediction through analysis from among the collected sensor data. One of such reasons is that the analysis model used for analysis frequently has a structure that is difficult to realize s...

AI Technical Summary

Benefits of technology

The present invention provides a data analysis method that allows a local analysis server to collect data from multiple devices and transmit it to a central analysis server for analysis. The central analysis server performs clustering analysis on the data and distributes an analysis model including cluster information to the local analysis server. The local analysis server reconstructs the clusters based on the analysis model and identifies the cluster corresponding to the received data. The invention also allows for identification of anomaly data and updating of the analysis model based on the anomaly data. Additionally, the invention allows for identification of the class corresponding to the received data and controlling of actuators based on the class information. The invention also includes generating a skeleton-shaped graph and position and connection information between nodes in a population. The technical effect of the invention is improved data analysis and analysis of large amounts of data from multiple devices.

Problems solved by technology

One of such reasons is that the analysis model used for analysis frequently has a structure that is difficult to realize such as a nonlinear structure, and not a linear structure or a simple structure to be transmitted with a support vector.

Large-capacity sensor data in the IoT environment have a huge amount of data, so it is difficult for the IoT devices to cluster them and analyze them in real time.

Further, a server of a service provider processes the large-capacity sensor data, a substantial distance between the IoT devices for transmitting sensor data and the server of the service provider or a distance on a network is very big, so a case of failing to quickly processing the IoT data is frequently generated.

However, it is insufficient to perform an analysis on unsupervised, large-capacity, and high-level learning, such as a clustering analysis, according to this method.

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